相律的应用:蒸发结晶过程自由度

在连续原理和浓度限制条件的基础上,分析了卤水蒸发过程的自由度,提出了“蒸发结晶过程自由度”这一概念。发现蒸发结晶过程自由度在整个过程中不是1便是0,在1和0之间交替变化,与选择的组分数和析出固相数目无关。蒸发结晶过程自由度为1,物理意义是在不引起新相产生旧相不消失的前提下过程连续地变化(水分的连续蒸失),几何意义是指在适当的坐标系中的一条线,一条直线或曲线;0的物理意义是指新相的产生和旧相的即将消失或者前一段的连续变化过渡到后一段的连续变化,几何意义是指一点,两蒸发阶段的交点或者过程的终点。简单地讨论了过程的单向性和连续件。     

可可西里——东昆仑活动构造带强震活动研究

青海昆仑山口西 8.1级地震发生在具有新生性特征的可可西里—东昆仑活动断裂带上。该断裂带在 190 0年以来的 10 0多年中经历了一个强震活动过程。在该强震活动过程中 ,地震沿整个可可西里—东昆仑活动构造带分段破裂 ,强震的破裂长度和震级之间大致满足对数线性的统计关系 ,强震活动呈现指数型时间分布的加速特征。这种强震加速活动特征可以用含多个震源体的孕震系统的强震成组活动模型给予解释。     

辽宁义县—北票地区义县组地层层序——义县阶标准地层剖面建立和研究之一

义县地区广义的义县组可解体为王家屯组 (暂命名 )和义县组 ;分属王家屯和义县火山旋回 ,前者为偏酸、偏碱性 ,后者主体为中基性、末期为中酸性火山岩系 ,并广泛发育潜火山岩相的玄武玢岩、安山玢岩和火山集块角砾熔岩筒。该区有七个主要沉积层 ,自下而上分别为王家屯组马神庙层、义县组老公沟层、业南沟层、砖城子层、大康堡层、朱家沟层和金刚山层 ;产有较丰富的无脊椎动物、脊椎动物和植物化石。以砖城子层、大康堡层和金刚山层为界 ,义县火山旋回可划分为 4个亚旋回 ,分别代表火山活动的初始期、主期、晚期和末期。北票四合屯地区的义县组相当义县地区义县组的第一和第二亚旋回 ,含鸟类化石的主沉积层 (尖山沟层和上园层 )可与砖城子层对比     

四川松潘马拉墩晚三叠世侏倭组的遗迹化石及沉积环境

四川松潘马拉墩晚三叠世侏倭组首次发现大量遗迹化石。本文根据不同遗迹属在不同层位中的相对丰度建立了两个遗迹组合，即Ｍｅｇａｇｒａｐｔｏｎ－Ａｒｔｈｒｏｐｈｙｃｕｓ组合及Ｎｅｏｎｅｒｅｉｔｅｓ－Ｐｈｙｃｏｓｉｐｈｏｎ组合，大致相当于Ｓｅｉｌａｃｈｅｒ（１９６７）［１］的Ｎｅｒｅｉｔｅｓ遗迹相．同时利用遗迹化石及沉积特点进行了沉积环境分析，认为侏倭组的沉积环境为大陆斜坡下部至盆地边缘。     

混合二维泊松过程的分解算法及其在提取地震丛集模式中的应用.

将一定范围内的地震数据假设为背景地震和丛集地震的叠加，并同时认为背景地震和丛集地震分别满足不同参数的二维泊松过程．通过引入N阶距离概念，将叠加的二维泊松过程转化为一维的混合密度函数，在对距离阶数进行选择的基础上，最终采用遗传算法进行混合密度分解，以达到提取地震丛集模式的效果．文中将该算法应用于我国西南地区松潘及龙陵主震前丛集地震的提取，并与C值的时间扫描结合，深化了这两次大地震前地震活动图象的认识．      

The algorithm of decomposing superim-posed 2-D Poisson processes and its applica-tion to the extracting earthquake clustering pattern

IntroductionClusteringearthquakesareusuallyconsideredasomensofstrongearthquakesorasignaloftectonicmovement.Thus,theyarenotonlyoneoftheprimaryevidencestopredictearthquakesbutalsoasignificantindicatortorecognizetectonicmovement(MEI,etal,1993;EarthquakePre-dictionandPreventionDepartmentofChinaSeismologicalBureau,1998).Ongeneralconditions,webelievethatclusteringearthquakesexistrelativelytobackgroundearthquakes,howtoeffectivelyseparateonefromtheotherbecomesthekeypointofextractingtheclusteringea…     

Research on Parallel Algorithms for uv-faceting Imaging

The uv-faceting imaging is one of the widely used large field of view imaging technologies, and will be adopted for the data processing of the low-frequency array in the first stage of the Square Kilometre Array (SKA1). Due to the scale of the raw data of SKA1 is unprecedentedly large, the efficiency of data processing directly using the original uv-faceting imaging will be very low. Therefore, a uv-faceting imaging algorithm based on the MPI (Message Passing Interface)+OpenMP (Open Multi-Processing) and a uv-faceting imaging algorithm based on the MPI+CUDA (Compute Unified Device Architecture) are proposed. The most time-consuming data reading and gridding in the algorithm are optimized in parallel. The verification results show that the results of the proposed two algorithms are basically consistent with that obtained by the current mainstream data processing software CASA (Common Astronomy Software Applications), which indicates that the proposed two algorithms are basically correct. Further analysis of the accuracy and total running time shows that the MPI+CUDA method is better than the MPI+OpenMP method in both the correctness rate and running speed. The performance test results show that the proposed algorithms are effective and have certain extensibility.     

Study of Star/Galaxy Classification Based on the XGBoost Algorithm

Machine learning has achieved great success in many areas today. The lifting algorithm has a strong ability to adapt to various scenarios with a high accuracy, and has played a great role in many fields. But in astronomy, the application of lifting algorithms is still rare. In response to the low classification accuracy of the dark star/galaxy source set in the Sloan Digital Sky Survey (SDSS), a new research result of machine learning, eXtreme Gradient Boosting (XGBoost), has been introduced. The complete photometric data set is obtained from the SDSS-DR7, and divided into a bright source set and a dark source set according to the star magnitude. Firstly, the ten-fold cross-validation method is used for the bright source set and the dark source set respectively, and the XGBoost algorithm is used to establish the star/galaxy classification model. Then, the grid search and other methods are used to adjust the XGBoost parameters. Finally, based on the galaxy classification accuracy and other indicators, the classification results are analyzed, by comparing with the models of function tree (FT), Adaptive boosting (Adaboost), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Stacked Denoising AutoEncoders (SDAE), and Deep Belief Nets (DBN). The experimental results show that, the XGBoost improves the classification accuracy of galaxies in the dark source classification by nearly 10% as compared to the function tree algorithm, and improves the classification accuracy of sources with the darkest magnitudes in the dark source set by nearly 5% as compared to the function tree algorithm. Compared with other traditional machine learning algorithms and deep neural networks, the XGBoost also has different degrees of improvement.     

松潘-摩天岭金成矿带控矿特征及找矿标志

松潘-摩天岭金成矿带位于川、陕、甘三省交界处,阳山金矿是近年在该成矿带上发现的一超大型岩金矿床.对其地质特征研究表明,除受构造控制外,岩浆岩、地层对矿床也有一定的控制作用,属浅成低温热液蚀变岩型金矿床.下一步找矿主要利用地物化遥等手段进行综合找矿,逐步解剖Ⅰ级-Ⅱ级-Ⅲ级远景区,寻找有望靶区,在找矿思路上要从矿床类型上突破,优选次序是破碎蚀变岩型-微细浸染型-石英脉型,应加强对含矿构造安昌河-观音坝主破碎蚀变带,尤其要加强其南北两侧及深部的研究和勘查.     

An Automatic Method for Detecting Transients and Variable Sources in AST3 Survey Based on Image Subtraction and Random Forest

AST3-2 (the second Antarctic Survey Telescope) is located in Antarctic Dome A, the loftiest ice dome on the Antarctic Plateau. It produces a huge amount of observational data which require a more efficient data reduction program to be developed. Also the data transmission in Antarctica is much difficult, thus it is necessary to perform data reduction and detect variable and transient sources remotely and automatically in Antarctica, but this attempt is restricted by the unsatisfactory performance of the low power consumption computer in Antarctica. For realizing this purpose, to develop a new method based on the existing image subtraction method and random forest algorithm, taking the AST3-2 2016 dataset as the test sample, becomes an alternative choice. This method performs image subtraction on the dataset, then applies the principle component analysis to extract the features of residual images. Random forest is used as a machine learning classifier, and in the test a recall rate of 97% is resulted for the positive sample. Our work has verified the feasibility and accuracy of this method, and finally found out a batch of candidates for variable stars in the AST3-2 2016 dataset.